@inproceedings{wu-etal-2020-dt,
title = "{DT}-{QDC}: A Dataset for Question Comprehension in Online Test",
author = "Wu, Sijin and
Yang, Yujiu and
Yung, Nicholas and
Shen, Zhengchen and
Lei, Zeyang",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.569",
doi = "10.18653/v1/2020.coling-main.569",
pages = "6470--6480",
abstract = "With the transformation of education from the traditional classroom environment to online education and assessment, it is more and more important to accurately assess the difficulty of questions than ever. As teachers may not be able to follow the student{'}s performance and learning behavior closely, a well-defined method to measure the difficulty of questions to guide learning is necessary. In this paper, we explore the concept of question difficulty and provide our new Chinese DT-QDC dataset. This is currently the largest and only Chinese question dataset, and it also has enriched attributes and difficulty labels. Additional attributes such as keywords, chapter, and question type would allow models to understand questions more precisely. We proposed the MTMS-BERT and ORMS-BERT, which can improve the judgment of difficulty from different views. The proposed methods outperforms different baselines by 7.79{\%} on F1-score and 15.92{\%} on MAE, 28.26{\%} on MSE on the new DT-QDC dataset, laying the foundation for the question difficulty comprehension task.",
}
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<abstract>With the transformation of education from the traditional classroom environment to online education and assessment, it is more and more important to accurately assess the difficulty of questions than ever. As teachers may not be able to follow the student’s performance and learning behavior closely, a well-defined method to measure the difficulty of questions to guide learning is necessary. In this paper, we explore the concept of question difficulty and provide our new Chinese DT-QDC dataset. This is currently the largest and only Chinese question dataset, and it also has enriched attributes and difficulty labels. Additional attributes such as keywords, chapter, and question type would allow models to understand questions more precisely. We proposed the MTMS-BERT and ORMS-BERT, which can improve the judgment of difficulty from different views. The proposed methods outperforms different baselines by 7.79% on F1-score and 15.92% on MAE, 28.26% on MSE on the new DT-QDC dataset, laying the foundation for the question difficulty comprehension task.</abstract>
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%0 Conference Proceedings
%T DT-QDC: A Dataset for Question Comprehension in Online Test
%A Wu, Sijin
%A Yang, Yujiu
%A Yung, Nicholas
%A Shen, Zhengchen
%A Lei, Zeyang
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F wu-etal-2020-dt
%X With the transformation of education from the traditional classroom environment to online education and assessment, it is more and more important to accurately assess the difficulty of questions than ever. As teachers may not be able to follow the student’s performance and learning behavior closely, a well-defined method to measure the difficulty of questions to guide learning is necessary. In this paper, we explore the concept of question difficulty and provide our new Chinese DT-QDC dataset. This is currently the largest and only Chinese question dataset, and it also has enriched attributes and difficulty labels. Additional attributes such as keywords, chapter, and question type would allow models to understand questions more precisely. We proposed the MTMS-BERT and ORMS-BERT, which can improve the judgment of difficulty from different views. The proposed methods outperforms different baselines by 7.79% on F1-score and 15.92% on MAE, 28.26% on MSE on the new DT-QDC dataset, laying the foundation for the question difficulty comprehension task.
%R 10.18653/v1/2020.coling-main.569
%U https://aclanthology.org/2020.coling-main.569
%U https://doi.org/10.18653/v1/2020.coling-main.569
%P 6470-6480
Markdown (Informal)
[DT-QDC: A Dataset for Question Comprehension in Online Test](https://aclanthology.org/2020.coling-main.569) (Wu et al., COLING 2020)
ACL
- Sijin Wu, Yujiu Yang, Nicholas Yung, Zhengchen Shen, and Zeyang Lei. 2020. DT-QDC: A Dataset for Question Comprehension in Online Test. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6470–6480, Barcelona, Spain (Online). International Committee on Computational Linguistics.